Handbook of Corrosion Engineering Episode 1 Part 9 pdf

The modern progress in understanding corrosion phenomena and trolling the impact of corrosion damage was greatly accelerated whenthe thermodynamic and kinetic behavior of metallic materi

4.2 Modeling and Life Prediction

The complexity of engineering systems is growing steadily with theintroduction of advanced materials and modern protective methods.This increasing technical complexity is paralleled by an increasingawareness of the risks, hazards, and liabilities related to the operation

of engineering systems However, the increasing cost of replacingequipment is forcing people and organizations to extend the useful life

of their systems The prediction of damage caused by environmentalfactors remains a serious challenge during the handling of real-lifeproblems or the training of adequate personnel Mechanical forces,which normally have little effect on the general corrosion of metals,can act in synergy with operating environments to provide localizedmechanisms that can cause sudden failures

Models of materials degradation processes have been developed for amultitude of situations using a great variety of methodologies For sci-entists and engineers who are developing materials, models havebecome an essential benchmarking element for the selection and lifeprediction associated with the introduction of new materials or process-

es In fact, models are, in this context, an accepted method of senting current understandings of reality For systems managers, thecorrosion performance or underperformance of materials has a very dif-ferent meaning In the context of life-cycle management, corrosion isonly one element of the whole picture, and the main difficulty with cor-rosion knowledge is to bring it to the system management level Thischapter is divided into three main sections that illustrate how corrosioninformation is produced, managed, and transformed

repre-4.2.1 The bottom-up approach

Scientific models can take many shapes and forms, but they all seek tocharacterize response variables through relationships with appropriatefactors Traditional models can be divided into two main categories:mathematical or theoretical models and statistical or empirical models.1Mathematical models have the common characteristic that the responseand predictor variables are assumed to be free of specification error andmeasurement uncertainty.2 Statistical models, on the other hand, arederived from data that are subject to various types of specification,observation, experimental, and/or measurement errors In generalterms, mathematical models can guide investigations, and statisticalmodels are used to represent the results of these investigations

Mathematical models. Some specific situations lend themselves to thedevelopment of useful mechanistic models that can account for the principal features governing corrosion processes These models are

most naturally expressed in terms of differential equations or anothernonexplicit form of mathematics However, modern developments incomputing facilities and in mathematical theories of nonlinear andchaotic behaviors have made it possible to cope with relatively complexproblems A mechanistic model has the following advantages:3

■ It contributes to our understanding of the phenomenon under study

■ It usually provides a better basis for extrapolation

■ It tends to be parsimonious, i.e., frugal, in the use of parameters and

to provide better estimates of the response

The modern progress in understanding corrosion phenomena and trolling the impact of corrosion damage was greatly accelerated whenthe thermodynamic and kinetic behavior of metallic materials was

con-made explicit in what became known as E-pH or Pourbaix diagrams

(thermodynamics) and mixed-potential or Evans diagrams (kinetics).These two models, both established in the 1950s, have become the basisfor most of the mechanistic studies carried out since then

The multidisciplinary nature of corrosion science is reflected in themultitude of approaches to explaining and modeling fundamental cor-rosion processes that have been proposed The following list givessome scientific disciplines with examples of modeling efforts that onecan find in the literature:

■ Surface science. Atomistic model of passive films

■ Physical chemistry. Adsorption behavior of corrosion inhibitors

■ Quantum mechanics. Design tool for organic inhibitors

■ Solid-state physics. Scaling properties associated with hot corrosion

■ Water chemistry. Control model of inhibitors and antiscaling agents

■ Boundary-element mathematics. Cathodic protection

The following examples illustrate the applications of computationalmathematics to modeling some fundamental corrosion behavior thatcan affect a wide range of design and material conditions

A numerical model of crevice corrosion. Many mathematical models havebeen developed to simulate processes such as the initiation and propa-gation of crevice corrosion as a function of external electrolyte composi-tion and potential Such models are deemed to be quite important forpredicting the behavior of otherwise benign situations that can progressinto aggravating corrosion processes One such model was publishedrecently with a review of earlier efforts to model crevice corrosion.4Themodel presented in that paper was applied to several experimental data

sets, including crevice corrosion initiation on stainless steel and activecorrosion of iron in several electrolytes The model was said to breaknew ground by

■ Using equations for moderately concentrated solutions and ing individual ion-activity coefficients Transport by chemical poten-tial gradients was used rather than equations for dilute solutions

includ-■ Being capable of handling passive corrosion, active corrosion, andactive/passive transitions in transient systems

■ Being generic and permitting the evaluation of the importance of ferent species, chemical reactions, metals, and types of kinetics atthe metal/solution interface

dif-Solution of the model for a particular problem requires specification

of the chemical species considered, their respective possible reactions,supporting thermodynamic data, grid geometry, and kinetics at themetal/solution interface The simulation domain is then broken into aset of calculation nodes, as shown in Fig 4.1; these nodes can bespaced more closely where gradients are highest Fundamental equa-tions describing the many aspects of chemical interactions and speciesmovement are finally made discrete in readily computable forms

During the computer simulation, the equations for the chemicalreactions occurring at each node are solved separately, on the assump-tion that the characteristic times of these reactions are much shorterthan those of the mass transport or other corrosion processes At theend of each time step, the resulting aqueous solution composition ateach node is solved to equilibrium by a call to an equilibrium solverthat searches for minima in Gibbs energy The model was tested by

;;;;;

;;;;;

;;;;;

yyyyy yyyyy

comparing its output with the results of several experiments withthree systems:

■ Crevice corrosion of UNS 30400 stainless steel in a pH neutral ride solution

chlo-■ Crevice corrosion of iron in various electrolyte solutions

■ Crevice corrosion of iron in sulfuric acid

Comparison of modeled and experimental data for these three tems gave agreement ranging from approximate to very good

sys-A fractal model of corroding surfaces. Surface modifications occurring ing the degradation of a metallic material can greatly influence thesubsequent behavior of the material These modifications can alsoaffect the electrochemical response of the material when it is submit-ted to a voltage or current perturbation during electrochemical testing,for example Models based on fractal and chaos mathematics havebeen developed to describe complex shapes and structures and explainmany phenomena encountered in science and engineering.5 Thesemodels have been applied to different fields of materials engineering,including corrosion studies Fractal models have, for example, beenused to explain the frequency dependence of a surface response toprobing by electrochemical impedance spectroscopy (EIS)6 and, morerecently, to explain some of the features observed in the electrochemicalnoise generated by corroding surfaces.7

dur-In an experiment designed to reveal surface features, a sample ofrolled aluminum 2024 sheet (dimensions 100  40  4 mm) was placed

in a 250-mL beaker in such a way that it was immersed in aerated 3%NaCl solution to a level about 30 mm from the top of the specimen.8The effect of aeration created a “splash zone” over the portion of thesurface that was not immersed During the course of exposure, a por-tion of the immersed region in the center of the upward-facing surfacebecame covered with gas bubbles and suffered a higher level of attackthan the rest of the immersed surface After 24 h, the plate wasremoved from the solution Figure 4.2 shows the specimen and theareas where the surface profiles were measured in diagrammatic form.Surface profile measurements were made by means of a Rank TaylorHobson Form Talysurf with a 0.2-m diamond-tip probe in all the var-ious planes and directions in these planes, i.e., LT, TL, LS, SL, ST, and

TS The instrument created a line scan of a real surface by pulling theprobe across a predefined part of the surface at a fixed scan rate of 1mm/s All traces were of length 8 mm, generating 32,000 points with asampling rate of 0.25 m per point, except for the SL and ST direc-tions, which, because of the plate thickness, were limited to 2-mm

traces or 8000 points The manufacturer’s software for the Talysurfinstrument was capable of generating more than 20 surface profileparameters In this study, two parameters, Ra and Rt, were retained.

Ra, the roughness average, described the average deviation from amean line, whereas Rt described the distance from the deepest pit tothe highest peak of the profile, an index which was taken as an engi-neering “worst-case” parameter for pitting severity

The corrosion found on the plate varied considerably from area toarea The region of the plate beneath the gas bubbles was found to beparticularly corroded, with a very high concentration of pits Across theremainder of the immersed upward-facing surface, pitting was scat-tered The splash zone of the surface above the electrolyte was also badlypitted On the sides, the pits had a geometry and orientation which con-formed to the expected grain structure of the rolled material In all cas-

es, changes noted in traditional Talysurf parameters were consistentwith expectations The severity of the corrosion was indicated by anincrease in Ra and Rt, and the profiles obtained gave good general indi-cations of the degree of pitting and the size of pits There was an approx-imately tenfold increase in Ra and Rt between the freshly polished

SprayZone

surface (reference data in Table 4.1) and the heavily corroded profiles

such as a, b, e, and g on Fig 4.2.

All profiles measured and analyzed with the Talysurf equipment werealso analyzed with the rescaled range (R/S) analysis technique The R/Stechnique, which can provide a direct evaluation of the fractal dimension

of a signal, was derived from one of the most useful mathematical els for analyzing time-series data, proposed a few years ago byMandelbrot and van Ness.9A detailed description of the R/S technique

mod-[in which R or R(t,s) stands for the sequential range of the data-point increments for a given lag s and time t, and S or S(t,s) stands for the

square root of the sample sequential variance] can be found in Fan et

al.10Hurst11and, later, Mandelbrot and Wallis12have proposed that the

ratio R(t,s)/S(t,s), also called the rescaled range, was itself a random

func-tion with a scaling property described by relafunc-tion (4.1), in which the

scal-ing behavior of a signal is characterized by the Hurst exponent (H), also

called the scaling parameter, which can vary over the range 0  H  1.

∝ sH

(4.1)

It has additionally been shown13 that the local fractal dimension D

of a signal is related to H through Eq (4.2), which makes it possible to

characterize the fractal dimension of a given time series by calculatingthe slope of an R/S plot

water The reduction in fractal dimension at the fine-texture resolution

of the Talysurf, from about 1.5 to about 1.2, would indicate a ing,” which might be explained by a greater loss of mass from the peaksthan from the valleys of the profiles.

“smooth-The correlation coefficients between the fractal dimension and thesurface parameters presented in Table 4.1 were calculated to be 0.89for Ra and 0.76 for Rt This would indicate that the fractal dimension

is slightly better related to a short-range descriptor or an averagequantity such as Ra than to a longer-range descriptor or a worst-casedistance quantity such as Rt R/S analysis can provide a direct methodfor determining the fractal dimension of surface profiles measuredwith commercial equipment Such analysis was helpful in shedding anew light on the real nature of the microscopic transformations occur-ring during the corrosion of aluminum

Statistical models. Frequently, the mechanism underlying a process isnot understood sufficiently well or is simply too complicated to allow

an exact model to be formulated from theory In such circumstances, anempirical model may be useful The degree of complexity that should beincorporated in an empirical model can seldom be assessed in the firstphase of designing the model The most popular approach is to start byconsidering the simplest model with a limited set of variables, thenincrease the complexity of the model as evidence is collected

Statistical assessment of time to failure is a basic topic in

reliabili-ty engineering for which many mathematical tools have been oped Evans, who also pioneered the mixed-potential theory to explainbasic corrosion kinetics (see Chap 1, Aqueous Corrosion), launchedthe concept of corrosion probability in relation to localized corrosion.According to Evans, an exact knowledge of the corrosion rate was lessimportant than ascertaining the statistical risk of its initiation.14Pitting is, of course, only one of the many forms of localized corrosion,and the same argument can be extended to any form of corrosion inwhich the mechanisms controlling the initiation phase differ fromthose controlling the propagation phase The following examplesillustrate the applications of empirical modeling in two areas of highcriticality

devel-Pitting corrosion in oil and gas operations. Engineers concerned with soil rosion of underground steel piping are aware that the maximum pitdepth found on a buried structure is somehow related to the percentage

cor-of the structure inspected Finding the deepest actual pit requires adetailed inspection of the whole structure, and as the percentage of thestructure inspected decreases, so does the probability of finding thedeepest actual pit A number of statistical transformations to quantifythe distributions in pitting variables have been proposed Gumbel isgiven the credit for the original development of extreme value statistics(EVS) for the characterization of pit depth distribution.15

The EVS procedure is to measure maximum pit depths on severalreplicate specimens that have pitted, then arrange the pit depth val-ues in order of increasing rank The Gumbel distribution, expressed

in Eq (4.3), where  and are the location and scale parameters,respectively, can then be used to characterize the data set and esti-mate the extreme pit depth that possibly can affect the system fromwhich the data were initially produced

F (x)  exp exp   (4.3)

In reality, there are three types of extreme value distributions:16

■ Type 1. exp[exp (x)], or the Gumbel distribution

■ Type 2. exp(x k), the Cauchy distribution

■ Type 3. exp[ k], the Weibull distribution

where x is a random variable and k and

To determine which of these three distributions best fits a specificdata set, a goodness-of-fit test is required The chi-square test or theKolmogorov-Simirnov test has often been used for this purpose A sim-pler graphical procedure using a generalized extreme value distribu-tion with a shape factor dependent on the type of distribution is alsopossible There are two expressions for the generalized extreme value

distribution, Eq (4.4) when kx

EVS were put to work on real systems in the oil and gas industries

on several occasions for two main reasons The first reason was thecritical nature of many operations associated with the transport of gasand other petroleum products, and the second was the predictability oflocalized corrosion of steel, the main material used by the oil and gasindustry

Meany has, for example, reported four detailed cases in whichextreme value distribution proved to be an adequate representation ofcorrosion problems:17

For underground piping

■ In a cathodic protection feasibility study

■ For the evaluation of a gas distribution system

For power plant condenser tubing

■ During the assessment of stainless steel tube leaks

■ During the assessment of Cu-Ni tube pitting performance

In another study, data from water injection pipeline systems andfrom the published literature were used to simulate the sample func-tions of pit growth on metal surfaces.18 This study, by Sheikh et al.,concluded that

■ Maximum pit depths were adequately characterized by extreme

data, a series of extreme value equations with the best fits (r2  0.95)was assembled and plotted collectively It was shown that EVS provided

a good representation of the distribution of corrosion pit depths

A validity analysis of the risk model with a 95 percent corrosionprobability indicated at least an 80 percent confidence level for theprediction Life expectancy calculations using the corrosion risk mod-

el provided the basis for the development of an optimized corrosionmanagement strategy to minimize the impact of corrosion on gas deliv-erability as the reservoir was depleted

Failure of nuclear waste containers. The regulations pertaining to the logic disposal of high-level nuclear waste in the United States andCanada require that the radionuclides remain substantially containedwithin the waste package for 300 to 1000 years after permanent clo-sure of the repository The current concept of a waste package involvesthe insertion of spent fuel bundles inside a container, which is thenplaced in a deep borehole, either vertically or horizontally, with a smallair gap between the container and the borehole For vitrified wastes, apour canister inside the outer container acts as an additional barrier.Currently, no other barrier is being planned, making the successfulperformance of the container material crucial to fulfilling the contain-ment requirements over long periods of time

geo-Provided that no failures occur as a result of mechanical effects, themain factor limiting the survival of these containers is expected to becorrosion caused by the groundwater to which they would be exposed.Two general classes of container materials have been studied interna-tionally: corrosion-allowance and corrosion-resistant materials.Corrosion-allowance materials have a measurable general corrosionrate but are not susceptible to localized corrosion By contrast, corrosion-resistant materials are expected to have very low general corrosionrates because of the presence of a protective surface oxide film However,they may be susceptible to localized corrosion damage.

A model developed to predict the failure of Grade 2 titanium wasrecently published in the open literature.20Two major corrosion modeswere included in the model: failure by crevice corrosion and failure byhydrogen-induced cracking (HIC) It was assumed that a small num-ber of containers were defective and would fail within 50 years ofemplacement The model was probabilistic in nature, and each model-ing parameter was assigned a range of values, resulting in a distribu-tion of corrosion rates and failure times The crevice corrosion rate wasassumed to be dependent only on the properties of the material andthe temperature of the vault Crevice corrosion was also assumed toinitiate rapidly on all containers and subsequently propagate withoutrepassivation Failure by HIC was assumed to be inevitable once acontainer temperature fell below 30°C However, the concentration ofatomic hydrogen needed to render a container susceptible to HICwould be achieved only very slowly, and the risk might even be negli-gible if that container had never been subject to crevice corrosion.Figure 4.3 illustrates the thin-shell packed-particulate design cho-sen as a reference container for this study The mathematical proce-dure to combine various probability functions and arrive at aprobability of failure of a hot container as a result of crevice corrosion

at a certain temperature is illustrated in Fig 4.4 The failure rate due

to HIC was arbitrarily assumed to have a triangular distribution inorder to simplify the calculations, given that HIC is predicted to beonly a marginal failure mode under the burial conditions considered

On the basis of these assumptions and the calculations described inthe full paper, it was predicted that 96.7 percent of all containerswould fail by crevice corrosion and the remainder by HIC However,only 0.137 percent of the total number of containers were predicted tofail before 1000 years (0.1 percent by crevice corrosion and 0.037 per-cent by HIC), with the earliest failure after 300 years

4.2.2 The top-down approach

The transformation of laboratory results into usable real-life functions forservice applications is almost impossible In the best cases, laboratory

tests can provide a relative scale of merit in support of the selection ofmaterials to be exposed to specific conditions and environments From anengineering management standpoint, mapping of the parameters defin-ing an operational envelope can reduce the need for exhaustive mecha-nistic models, since any potential problem should be avoidable bycontrolling the conditions of its occurrence.

Some of the issues involved in deciding on a cost-effective methodfor combating corrosion are generic to sound management of engi-neering systems Others are specifically related to the impact of cor-rosion damage on system integrity and operating costs In processoperations, where corrosion risks can be extremely high, costs areoften categorized by equipment type and managed as an asset lossrisk (Fig 4.5).21 The quantification or ranking of risk, defined as the

Top head (6.35 mm thick)

Titanium shell (6.35 mm thick)

Gas tungsten arc weld 0.65 m

0.63 m

Figure 4.3 Packed-particulate supported-shell container for

waste nuclear fuel bundles.

product of the probability and consequences of specific events, shoulddictate the preferential order in which inspection and maintenanceare performed By referring to Fig 4.5, the operations department of

a process plant should adjust the maintenance schedule, consideringthe decreasing attention given to piping, reactors, tanks, and processtowers Similar logic applies to all industries The following exampleswill illustrate how these considerations are manifested in practiceand how corrosion information is integrated into efficient manage-ment systems

A fault tree for the risk assessment of gas pipeline. Fault tree analysis(FTA) is the process of reviewing and analytically examining a system

Fraction failed at time t

or equipment in such a way as to emphasize the lower-level faultoccurrences which directly or indirectly contribute to a major fault orundesired event The value of performing FTA is that by developingthe lower-level failure mechanisms necessary to produce higher-leveloccurrences, a total overview of the system is achieved Once complet-

ed, the fault tree allows an engineer to fully evaluate a system’s safety

or reliability by altering the various lower-level attributes of the tree.Through this type of modeling, a number of variables may be visual-ized in a cost-effective manner

A fault tree is a diagrammatic representation of the relationshipbetween component-level failures and a system-level undesired event

A fault tree depicts how component-level failures propagate throughthe system to cause a system-level failure The component-level fail-ures are called the terminal events, primary events, or basic events ofthe fault tree The system-level undesired event is called the top event

of the fault tree Figure 4.6 presents, in graphical form, the tree andgate symbols most commonly used in the construction of fault trees.22

A brief description of these symbols is given in the following list:

■ Fault event (rectangle). A system-level fault or undesired event

■ Conditional event (ellipse). A specific condition or restrictionapplied to a logic gate (mostly used with an inhibit gate)

■ Basic event (circle). The lowest event examined which has thecapability of causing a fault to occur.

■ Undeveloped event (diamond). A failure which is at the lowest level

of examination in the fault tree, but which can be further expanded

■ Transfer (triangle). The transfer function is used to signify a nection between two or more sections of the fault tree

con-■ AND gate. The output occurs only if all inputs exist (Probabilities

of the inputs are multiplied, decreasing the resulting probability.)

■ OR gate. The output is true only if one or more of the input eventsoccur (Probabilities of the inputs are added, increasing the resultingprobability.)

■ Inhibit gate (hexagon). One input is a lower fault event and theother input is a conditional qualifier or accelerator [direct effect as adecreasing (1) or increasing factor (1)]

The FTA methodology was adopted by Nova Corp., a major

natur-al gas transport and processing company in Canada, for the risk

Figure 4.6 Fault tree symbols for gates, transfers, and events.

assessment of its 18,000-km gas pipeline network.23FTA is normallyperformed for the review and analytical examination of systems orequipment to emphasize the lower-level fault occurrences, and theresults of the FTA calculations are regularly validated with inspec-tion results These results are also used to schedule maintenanceoperations, conduct surveys, and plan research and developmentefforts.

Figures 4.7 and 4.8 illustrate respectively the SCC branch and theuniform corrosion branch of the Nova Corp pipeline outage FTA sys-tem Each element of the branches in Figs 4.7 and 4.8, which are part

of a larger tree that estimates the overall probability of pipeline ure, contains numeric probability information related to technical andhistorical data for each segment of the 18,000-km pipeline

fail-The Maintenance Steering Group (MSG) system. The aircraft industry andits controlling agencies have developed another top-down approach torepresent potential failures of aircraft components The MaintenanceSteering Group (MSG) system has evolved from many years of corporateknowledge The first generation of formal air carrier maintenance pro-grams was based on the belief that each part on an aircraft requiredperiodic overhaul As experience was gained, it became apparent thatsome components did not require as much attention as others, and newmethods of maintenance control were developed Condition monitoringwas thus introduced into the decision logic of the initial MaintenanceSteering Group document (MSG-1) and applied to Boeing 747 aircraft.The MSG system has now evolved considerably The experiencegained with MSG-1 was used to update the decision logic and create

a more universal document that is applicable to other aircraft andpowerplants.24When applied to a particular aircraft type, the MSG-2logic would produce a list of maintenance significant items (MSIs), toeach of which one or more process categories would be applied, such

as “hard time,” “on-condition,” and/or “reliability control.”

The most recent update to the system was initiated in 1980 Theresultant MSG-3 system has the same basic philosophy as MSG-1 andMSG-2, but prescribes a different approach to the assignment of main-tenance requirements Instead of the process categories typical of MSG-

1 and MSG-2, the MSG-3 logic identifies maintenance requirements.The processes, tasks, and intervals arrived at with MSG can be used byoperators as the basis for their initial maintenance program In 1991,industry and regulatory authorities began working together to provideadditional enhancements to MSG-3 As a result of these efforts,Revision 2 was submitted to the Federal Aviation Administration (FAA)

in September 1993 and accepted a few weeks later Major ments include

enhance-Pipeline Outage (SCC)

Hydrogen Induced

Cracking

SCC Initiation

Pipe Susceptible

to SCC

Disbondment Supporting SCC

SCC Conditions Under Coating

Operating Stress >

Threshold Stress

Groundwater Critical Composition Peened

Coating Disbondment

Coating DI-Electric Strength

Average Leak Frequency SCC Outage

Coating Type Age Location

Figure 4.7 Fault tree for natural gas pipeline outage caused by SCC.

Pipe Size

Pipeline Outage (Corrosion)

Inadequate

C.P Potential

Cathodic Protection Shielded

Coating Disbonded

Coating Improperly Installed

Electrolyte Present

Pipeline Exposed

to Environment

Cathodic Protection Deficiency

Probability of Pipe at

Operating Pressure

Corrosion Leak Probability Factor

Probability of Corrosion Damage at Failure Dimension

Probability of Coating Defect <

Rupture Length

Probability of Penetration before Critical Length

Probability of Severe and Active Corrosion

Probability

of Corrosion Occurring

Figure 4.8 Fault tree for natural gas pipeline outage caused by general corrosion.

■ Expansion of the systems/powerplant definition of inspection

■ Guidelines for the development of a corrosion prevention and controlprogram (CPCP)

■ Increased awareness of the requirements of aging aircraft

■ Extensive revision of the structure logic

The MSG-3 structure analysis begins with the development of acomplete breakdown of the aircraft systems, down to the componentlevel All structural items are then classified as either structure sig-nificant items (SSIs) or other structure An item is classified as an SSI

on the basis of consideration of the consequences of failure and thelikelihood of failure, along with material, protection, and probableexposure to corrosive environments All SSIs are then listed and cate-gorized as damage-tolerant or safe life items to which life limits areassigned.25For all SSIs, accidental damage, environmental deteriora-tion, corrosion prevention and control, and fatigue damage evaluationsare performed following the logic diagram illustrated in Fig 4.9.Once the MSG-3 structure analysis is completed, each element of thestructural analysis diagram (Fig 4.9) can be expanded right to the indi-vidual components and associated inspection and maintenance tasks

Accidental Damage Analysis

Fatigue Damage Analysis

Environmental Deterioration Analysis

Corrosion Prevention &

Control Program

ListSSIs

Significant Structure

Identify Candidate

Significant Structure

Define Aircraft Zones or Areas

Figure 4.9 Overall MSG-3, Revision 2, structural analysis logic diagram.

The procedure for MSG-3 environmental deterioration analysis (EDA),for example, involves the evaluation of the structure in terms of proba-ble exposure to adverse environments The evaluation of deterioration

is based on a series of steps supported by reference materials ing baseline data expressing the susceptibility of structural materials

contain-to various types of environmental damage While the end product of theMSG-3 is very component-specific, its information contains much ofwhat is required to create a more generic system based on materialsinstead of part numbers The logic of the EDA, illustrated in Fig 4.10,requires the input of a multitude of parameters, given in the followinglist, guided by the use of a template, shown in Fig 4.11

■ Item location/accessibility/visibility

■ Item material/temper/manufacturing specification

■ Material of adjacent items

Yes No Random

Combine Rating

Type of Corrosion

No Systematic

Yes

Material & Temper

Threshold Possible

Visual Inspection Possible

NDI Possible

Establish Inspection Task

Establish Threshold

Determine Rating:

- stress corrosion cracking

- other corrosion mode

- protection potential

- environment

Figure 4.10 Environmental deterioration analysis logic diagram.

OTHER CORROSION RATING

Select Lowest Rating # CONSIDER MODIFICATION

## ZONAL PROGRAM

REPEAT INTERVAL

Is there a systematic characteristic? REMARKS

INSPECTION LEVEL INSPECTION LEVEL

INITIAL THRESHOLD Yrs

Potential Type of Corrosion Intergranular

Pitting Uniform Galvanic

Erosion Filliform Microbiological Crevice

Fretting

2 1

3

Material with High Sensitivity Material with Average Sensitivity Material with Low Sensitivity

Rating

3 2 1

1 2 3 Good Average Excellent

1 1 2

1 2 3

2 3 3

Stress Corrosion

Rating

Material Sensitive Component Subject to Built-In Stresses Material Sensitive Component Not Subject to Built-In Stresses Material Not Sensitive.

1 2 3

2

2 1 2

2 3

Figure 4.11 Environmental deterioration analysis template.